Brief CV

Richard Pibernik received his doctorate degree from Goethe University in Frankfurt, Germany. From 2004 to 2007 he was a research affiliate at the Massachusetts Institute of Technology (MIT) and a Professor within the MIT-Zaragoza International Logistics Programme at the Zaragoza Logistics Centre in Spain. From 2007 to 2012 he was a Professor of Supply Chain Management at the EBS Business School in Wiesbaden, and from 2010-2012 Otto Mønsted Visiting Professor at Copenhagen Business School. As of 2012 Richard is a chaired Professor of Logistics and Quantitative Methods in Business Administration at the University of Würzburg. At the same time he is an Adjunct Professor at the Zaragoza Logistics Centre, Spain, and a Visiting Professor at the Malaysia Institute of Supply Chain Innovation. Both Zaragoza Logistics Center and the Malaysia Institute of Supply Chain Innovation are nodes of MIT's Global SCALE Network, an international alliance of leading-edge research and education centers dedicated to the development and dissemination of global innovation in supply chain and logistics.

Richard’s research is focused on quantitative methods for supply chain and logistics management. In particular, he works on data-driven approaches to supply chain management that integrate machine learning and traditional optimization models in supply chain management, supply chain information systems, and integrated planning approaches. He currently heads a research group consisting of eight researchers that is dedicated to data-driven approaches in supply chain and operations management, and also works actively in research project addressing healthcare supply chains for developing countries.

Richard has published his research in numerous renowned international journals such as Management Science, Production and Operations Management, Naval Research Logistics, and has been responsible, as a principal investigator, for many projects that were funded by industry and public funding agencies. He received grants from public funding agencies (EU, German Research Foundation, and the Governments of Germany and Spain), and carried out projects with large corporations (e.g. Lufthansa, Alcatel-Lucent, SAP), consulting firms (e.g. McKinsey), and many SME’s in Germany, Spain, and other countries. He is currently working with many companies in projects focused on data-driven supply chain management, supply chain analytics, and exploitation of big data in supply chain management.

Matching supply with demand remains a challenging task for many companies, especially when purchasing and production must be planned with sufficient lead time, demand is uncertain, overall supply may not suffice to fulfill all of the projected demands, and customers differ in their level of importance. The particular structure of sales organizations often adds another layer of complexity: These organizations often have multi-level hierarchical structures that include multiple geographic sales regions, distribution channels, customer groups, and individual customers (e.g., key accounts). In this paper, we address the problem of ``allocation planning'' in such sales hierarchies when customer demand is stochastic, supply is scarce, and the company's objective is to meet individual customer groups' service-level targets. Our first objective is to determine when conventional allocation rules lead to optimal (or at least acceptable) results and to characterize their optimality gap relative to the theoretical optimum. We find that these popular rules lead to optimal results only under very restrictive conditions and that the loss in optimality is often substantial. This result leads us to pursue our second objective: to find alternative (decentral) allocation approaches that generate acceptable performance under conditions in which the conventional allocation rules lead to poor results. We develop two alternative (decentral) allocation approaches and derive conditions under which they lead to optimal allocations. Based on numerical analyses, we show that these alternative approaches outperform the conventional allocation rules, independent of the conditions under which they are used. Our results suggest that they lead to near-optimal solutions under most conditions.

The Value of Entrant Manufacturers: A Study of Competition and Risk for Donor-Funded Procurement of Essential Medicines.Lauton, Felix; Rothkopf, Alexander; Pibernik, Richard in Accepted for publication in European Journal of Operational Research (2018).

Abstract Global-health purchasing organizations (POs) want to increase access to essential medicines in low-income countries. One way to purchase more medicines with limited funds is to contract with generics manufacturers, thereby increasing competition and lowering prices. However, many POs fear that these entrants are less reliable than others and increase supply risks: failure to adhere to lead times and supplier defaults may cause disruptions that result in unsuccessful medical treatments. The problem can be remedied or at least reduced if POs have a sound basis for assessing manufacturers. To this end, we develop a mathematical framework that supports decision-makers in an integrated evaluation of an entrant’s effect on purchasing costs and supply risks. Our approach accounts for the characteristics of donor-funded global-health markets and the particular tasks and specific challenges of POs in these markets. More specifically, our approach enables a PO to quantify a potential entrant’s value depending on important characteristics of the incumbent and the entrant manufacturer. We use data from a project for donor-funded procurement of Depot Medroxyprogesterone Acetate (DMPA) of two large POs. Our results show the feasibility of our approach for POs, manufacturers, and philanthropic investors in the global-health domain, and we explore the trade-off between competition and supply risks and provide insights into how the entrant’s value is affected by parameters like production costs, capacity, lead time and default risk, and in-country registration.

As machines get smarter, massive amounts of condition-based data from distributed sources become available. This data can be used to enhance maintenance management in several ways, such as by improving maintenance demand forecasting and spare parts and capacity planning. Regarding the former, machine learning techniques promise substantial benefits for forecasting the demand for spare parts over conventional techniques that are commonly used. While development and implementation of these techniques is difficult, practical applications pose another important challenge to providers of maintenance, repair, and overhaul services. Their customers are reluctant to provide access to sensitive real-time data because of privacy concerns, and even more so when their data is stored and processed in the cloud. In this paper we describe an application for privacy-preserving forecasting of demand for spare parts based on distributed condition data. It combines machine learning techniques---more specifically, decision-tree classification---with order-preserving encryption. The application is appropriate whenever planning for spare parts for the maintenance of condition-monitored machinery is needed, and it is particularly suitable for cloud-based implementation.

Considering the increasing international division of labor, as well as stakeholders' growing awareness of sustainability, assuring that business practices are sustainable is a major challenge. Companies have to account for the fact that any misconduct at a supplier's premises may have spillover effects that reach the manufacturer or retailer. Therefore, purchasing managers have to assure that their suppliers are compliant with sustainability standards. This, however, may induce higher purchasing costs and, as a consequence, force a trade-off between (short term) economic (i.e., purchasing cost reduction) and social/environmental sustainability criteria. How purchasing managers evaluate this trade-off is particularly interesting because they often receive performance-based salaries that incentivize the reduction of purchasing costs. Our paper sheds light on this trade-off by examining how much purchasing managers are willing to pay to assure compliance along different sustainability dimensions when selecting new suppliers in a mature market setting, namely Germany. Additionally, we identify potential (individual, professional, and organization-related) factors that may impact the purchasing managers' willingness to pay (WTP), and examine their effects. Among the most surprising findings, purchasing managers on average are willing to pay a price premium for manuals that demonstrate compliance with the United Nationals Global Compact (UNGC). Furthermore, the results show that this WTP is mostly influenced (negatively) by self-enhancement (on the individual level) and/or obedience to authority (on the organizational level), but the effects of company, affiliation with the UNGC, gender, or years of experience have no influence. Moreover, the WTP is higher for the social than for the environmental dimension, and the marginal effect of accreditation on WTP depends on which combinations of dimensions are accredited.

Global-health purchasing organizations (POs) try to increase the number of people who are treated with essential medicines in low- and low-middle-income countries. One way to purchase more medicines with limited funds is to contract generics manufacturers in order to increase competition and lower prices. However, many POs fear that these entrants are less reliable than others and that they increase supply risks: uncertain lead times and the potential for default may cause disruptions that result in unsuccessful medical treatments. Our study answers the question when does an entrant, specifically, a new generics manufacturer, provide value to the PO? Inspired by a project for donor-funded procurement of Depot Medroxyprogesterone Acetate (DMPA) of two large POs, we develop a mathematical model to capture an entrant's value depending on the volume split among manufacturers. We use data from the DMPA case to show the feasibility of our approach for POs, manufacturers, and philanthropic investors in the global-health domain. We present numerous interesting results - some counter intuitive. For example, our analyses show that the trade-off between competition benefits and risk is not as trivial as one may think and can sometimes be inverted: a risky entrant can still provide risk-diversification benefits, and procurement costs do not always decrease if the entrant has lower costs. We explore the trade-off between competition and risk and provide insights into how the entrant's value is affected by parameters like production costs, capacity, lead time, default risk and in-country

In recent years many companies have made an eort to consolidate purchasing volumes that were previously scattered across different organizational units with the objective to achieve lower purchasing prices, lower transaction costs, etc. To realize these benefits companies frequently opt for a hybrid organizational structure. A central purchasing department negotiates frame contracts based on consolidated purchasing volumes from which local (de-central) business units are required to order. This organizational setup is conducive to a specific form of non-compliant behavior, oftentimes referred to as maverick buying: local business units bypass the official purchasing processes and source from a supplier other than the designated one. This non-compliant behavior obviously counteracts the companies' efforts to achieve benefits of volume consolidation. In this paper we consider maverick buying as a specific form of hidden action in a principal agent framework and provide a formal analysis of different strategies that companies can employ to remedy its negative consequences. Our results suggest that the conventional strategy of monitoring agents and penalizing them in case of non-compliance has clear limitations and may, in fact, be ineffective. We argue that rather than trying to eliminate maverick buying through monitoring, it can, under certain conditions, be more effective to participate in the agents' superior market knowledge. To this end we propose a self-selection model in which the principal offers distinct contract menus - termed participation menus - which are tailored toward agents with an attractive outside option and agents without an incentive to buy maverick. We demonstrate that participation menus perform particularly well in situations in which conventional monitoring fails. Our results indicate that there may even be situations in which they can be employed to leverage the agents' purchasing capabilities so that profits exceed those of a traditional first-best solution.

When decision makers face crucial strategic decisions they frequently have to rely on judgments about events in the far future. These judgments are typically characterized by very high uncertainty and the absence of experience from previous good or bad judgments. Judgments of other experts are oftentimes an important—sometimes the only—source of additional information to reduce uncertainty and improve judgment accuracy. However, decision makers have very limited means to evaluate the quality of such “advice” from other experts and could tend to ignore this valid source of information. In this paper we study what leads decision makers to take advice from an expert panel when judging the probability of far future events with high economic impact. Our analysis is based on a unique dataset that comprises more than 15,000 advice taking decisions made by almost 1,000 experts from different industries. We find that decision makers’ general tendency to ignore advice is particularly strong in the domain of long term judgments and pronounces even further when conflicts in terms of beliefs, past experiences, or desires arise.

Service differentiation in a single-period inventory model with numerous customer classes.Schulte, B; Pibernik, R in OR Spectrum (2016). 38(4) 921--948.

We study critical-level inventory-management policies as means to provide differentiated ( αα and ββ ) service levels to more than two classes of customers. First, we derive closed-form expressions for the service levels of a single-period critical-level policy with an arbitrary number of customer classes (with Poisson demand). Based on the service-level expressions, we derive additional structural insights and provide an efficient algorithm with which to compute the essential system parameters, that is, the minimum required starting inventory and the associated critical levels. Based on these results, we conduct numerical experiments and develop structural insights into the system’s behavior.

The Influence of Ethical Culture on Supplier Selection in the Context of Sustainable Sourcing.Goebel, P; Pibernik, R; Sichtmann, C; Reuter, C in International Journal of Production Economics (2012). 140(1) 7--17.

The impact of performance measurement on the recognition of purchasing and supply management’s value contribution: A principal-agent based case study analysis.Groetsch, V; Henke, M; Pibernik, R in International Journal of Production Economics - submitted (2009).